Our approach for the shape parsing is neural network based and it works sequentially, cutting a part of the shape until all the area is covered. Taking into account input data representation we consider using corner points detection algorithm in the orthographic projection images to help the agent. Corner points usually indicates to complex geometry or branching points in the object topology. We learn effective shape parsing which leads to increase in the accuracy of the reconstruction from planar cross-sections of the shape. We employ Reinforcement Learning (RL) to solve this task, by taking actions and collecting rewards. In each step we cut a part of 3D object and reconstruct it from planar cross-sections in this region. The reward function is designed to take into account the accuracy of the reconstruction. Objects are arbitrarily selected from the ShapeNet [1]} dataset and the number of planar cross sections is fixed. In the experiments section we show that our algorithm works efficiently for both ShapeNet objects and also downloaded objects from the Internet.
